Methods of determining a state of a dependent user

ABSTRACT

In some examples, a computer-implemented method for determining a state of a dependent user is provided. A flag is generated if a measured first parameter for a dependent user is within a range of values of a respective defined first parameter and a measured second parameter for the dependent user is within a range of values of a respective defined second parameter, and the measured first parameter and the measured second parameter occur within a temporal window.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 to U.S.Provisional Application No. 62/776,786, filed Dec. 7, 2018, which ishereby incorporated by reference in its entirety for all purposes.

BACKGROUND

This specification relates in general to determining a state of adependent user. Such an determination may be critical in identifying aprotocol to address the state of the dependent user. However, existingmethods of determining the state of the dependent user may be slow,burdensome, and inaccurate.

SUMMARY

Exemplary embodiments of the invention provide systems and methods fordetermining a state of a dependent user. According to an aspect of theinvention, a computer-implemented method of determining a state of adependent user includes receiving data for the dependent user. The dataincludes a plurality of measured first parameters and a plurality ofmeasured second parameters. The computer-implemented method alsoincludes accessing a rule set for the dependent user. The rule setincludes a range of values for each of a plurality of defined firstparameters and a range of values for each of a plurality of definedsecond parameters. For a subset of the data that is collected within atemporal window, and for at least one measured first parameter of theplurality of measured first parameters, the computer-implemented methodincludes comparing the measured first parameter with a respective one ofthe plurality of the defined first parameters to determine whether themeasured first parameter is within the range of values for therespective one of the plurality of defined first parameters. For thesubset of the data that is collected within a temporal window, and forat least one measured second parameter of the plurality of measuredsecond parameters, the computer-implemented method includes comparingthe measured second parameter with a respective one of the plurality ofthe defined second parameters to determine whether the measured secondparameters is within the range of values for the respective one of theplurality of defined second parameters. If it is determined that atleast one of the measured first parameters is within the range of valuesfor the respective one of the plurality of the defined first parametersand it is determined that at least one of the measured second parametersis within the range of values for the respective one of the plurality ofthe defined second parameters, the computer-implemented method alsoincludes generating a flag, and outputting a message including anidentifier of the dependent user, the flag, the at least one of themeasured first parameters that is within the range of values for therespective one of the plurality of the defined first parameters, and theat least one of the measured second parameters is within the range ofvalues for the respective one of the plurality of the defined secondparameters.

The computer-implemented method may also include processing the data tofilter out invalid entries. The rule set may be a function of aclassification of the dependent user. Alternatively or in addition, therule set may be a function of a location of the dependent user.

The message may also include a level of response for the state of thedependent user. Further, the message may also include a time frame forthe response. The flag may indicate the state of the dependent user.

Other objects, advantages, and novel features of the present inventionwill become apparent from the following detailed description of theinvention when considered in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various examples in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 is an example block diagram illustrating an interaction system inwhich techniques relating to determining a state of a dependent user maybe implemented, according to at least one example;

FIG. 2 is an example block diagram illustrating an interaction system inwhich techniques relating to determining a state of a dependent user maybe implemented, according to at least one example;

FIG. 3 is an example schematic model illustrating a networkcommunication model in which techniques relating to determining a stateof a dependent user may be implemented, according to at least oneexample;

FIG. 4 is an example schematic model illustrating an aspect of thenetwork communication model of FIG. 3 in more detail;

FIG. 5 is an example schematic model illustrating an aspect of thenetwork communication model of FIG. 3 in more detail;

FIG. 6 is an example schematic model illustrating an aspect of thenetwork communication model of FIG. 3 in more detail;

FIG. 7 is an example schematic model illustrating an aspect of thenetwork communication model of FIG. 3 in more detail;

FIG. 8 is an example schematic architecture illustrating an interactionsystem in which techniques relating to determining a state of adependent user may be implemented, according to at least one example;

FIG. 9 is an example block diagram of an example of a network in whichtechniques relating to determining a state of a dependent user may beimplemented, according to at least one example;

FIG. 10 is an example flowchart of a method in which techniques relatingto determining a state of a dependent user may be implemented, accordingto at least one example;

FIG. 11 is an example of a rule set that may be used by the techniquesdescribed herein;

FIG. 12 is an example of a dashboard that may be generated by thetechniques described herein;

FIG. 13 is an example flowchart of a method in which techniques relatingto generating a plurality of alerts relating to the state of a dependentuser may be implemented, according to at least one example;

FIG. 14 shows examples of detailed alerts that may be generated by thetechniques described herein;

FIG. 15 shows examples of messages that may be generated by thetechniques described herein;

FIG. 16 is an example flowchart of a method in which techniques relatingto generating a candidate evaluation and/or a candidate protocol for adependent user may be implemented, according to at least one example;

FIG. 17 is an example flowchart of a method in which techniques relatingto determining a likelihood of readmission for a dependent user may beimplemented, according to at least one example;

FIG. 18 is an example flowchart of a method in which techniques relatingto determining a level for a dependent user may be implemented,according to at least one example.

DETAILED DESCRIPTION

The ensuing description provides preferred exemplary embodiment(s) only,and is not intended to limit the scope, applicability or configurationof the disclosure. Rather, the ensuing description of the preferredexemplary embodiment(s) will provide those skilled in the art with anenabling description for implementing a preferred exemplary embodiment.It is understood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

Referring first to FIG. 1, a block diagram of an example of aninteraction system 100 is illustrated. Generally, in interaction system100, data can be generated at one or more system components 102 and/oruser devices 104. Management engine 106 can manage the flow ofcommunications within interaction system. Transformative processingengine 108 can receive, intercept, track, integrate, process, and/orstore such data.

Data flowing in interaction system 100 can include a set ofcommunications. Each of one, some of all communications can include (forexample) an encoding type, authentication credential, indication of acontent size, identifier of a source device, identifier of a destinationdevice, identifier pertaining to content in the communication (e.g., anidentifier of an entity), a processing or reporting instruction, aprocedure specification, transmission time stamp, and/or sensormeasurement. Data may, or may not, selectively pertain to a particularentity and/or client. Data can, depending on the implementation, includeindividually identifiable information and/or de-identified informationas it pertains to an entity and/or client. Data may, but need not,include protected information.

For example, a system component 102 can include, for example, a sensorto detect a sensor measurement and can thereafter generate and transmita communication that reflects the sensor measurement. The communicationmay be transmitted at routine times and/or upon detecting a threshold(e.g., one or more) number of measurements or a measurement satisfying atransmission condition (e.g., exceeding a threshold value). In someinstances, the sensor measurement corresponds to one reflecting aproperty of an object or entity (e.g., person) near the sensor. Thecommunication may then include an identifier of the object or entity.The identifier can be determined, for example, based on detection of anearby electronic tag (e.g., RFID tag), a detected user input receivedat a user interface of component 102, and/or data in a correspondingcommunication received from a user device.

As another example, a user device 104 can be configured to detect inputreceived at an interface of the device. The input can include, forexample, an identifier of an object or entity, an instruction, acharacterization of an object or entity, an identification of anassessment to be performed, a specification of an aggregation or dataprocessing to be performed, and/or an identification of a destinationfor a data-analysis report. User device 104 can further be configured todetect input requesting particular data, to generate a requestcommunication (e.g., to be sent to transformative processing engine), toreceive the requested data and/or to present the received data.

The depicted engines, devices and/or components can communicate over oneor more networks. A network of one or more networks can include a wirednetwork (e.g., fiber, Ethernet, powerline ethernet, ethernet overcoaxial cable, digital signal line (DSL), or the like), wireless network(e.g., Zigbee™, Bluetooth™, WiFi™, IR, UWB, WiFi-Direct, BLE, cellular,Long-Term Evolution (LTE), WiMax™, or the like), local area network, theInternet and/or a combination thereof. It will be appreciated that,while one or more components 102 and one or more user devices 104 areillustrated as communicating via transformative processing engine 108and/or management engine 106, this specification is not so limited. Forexample, each of one or more components 102 may communicate with each ofone or more user devices 104 directly via other or the samecommunication networks.

A component 102 can be configured to detect, process and/or receivedata, such as environmental data, geophysical data, biometric data,chemical data (e.g., chemical composition or concentration analysisdata), and/or network data. The data can be based on data detected, forexample, via a sensor, received signal or user input. A user device 104can include a device configured to receive data from a user and/orpresent data to a user. It will be appreciated that, in some instances,a component 102 is also a user device 104 and vice-versa. For example, asingle device can be configured to detect sensor measurements, receiveuser input and present output.

A component 102 can be configured to generate a communication that is inone or more formats, some of which can be proprietary. For example, animaging machine (e.g., one of one or more components 102) manufacturedby company A, located within a first facility (e.g., facility 110), andbelonging to a first client, may save and transfer data in a firstformat. An imaging machine (e.g., one of one or more components 102)manufactured by company B, located within the first facility (e.g.,facility 110), and belonging to the first client, may save and transferdata in a second format. In some examples, data from certain componentsis transformed, translated, or otherwise adjusted to be recognizable bytransformative processing engine 108. Thus, continuing with the examplefrom above, when the imaging machines manufactured by companies A and Bare located within the first facility belonging to the first client,they may nevertheless save and transfer data in different formats. Insome examples, one or more components 102 communicate using a definedformat.

In some examples, each of one or more components 102 are each associatedwith one or more clients within a same or different interaction systems.For example, certain ones of one or more components 102 may beassociated with a first client, while other ones of one or morecomponents 102 may be associated with a second client. Additionally,each of one or more components 102 may be associated with a facility 110(e.g., client facility). Each facility 110 may correspond to a singlelocation and/or focus. Exemplary types of facilities include server farmfacilities, web-server facilities, data-storage facilities,telecommunication facilities, service facilities, and/or operationalfacilities. For example, a first facility may include a structure at afirst location at which one or more resources (e.g., computationalresources, equipment resources, laboratory resources, and/or humanresources) are provided. Each of the one or more resources may be of afirst type in a first set of types. A resource type can be identifiedbased on, for example, a characteristic of the resource (e.g., sensorinclusion) and/or a capability of providing each of one or moreservices. Thus, for example, resources at a first facility may be betterconfigured for handling a particular type of service requests comparedto those in another facility. As another example, different facilitiesmay include resources of similar or same types but may vary in terms of,for example, accessibility, location, etc.

Transmission of data from one or more components 102 to transformativeprocessing engine 108 may be triggered by a variety of different events.For example, the data may be transmitted periodically, upon detection ofan event (e.g., completion of an analysis or end of a procedure), upondetection of an event defined by a rule (e.g., a user-defined rule),upon receiving user input triggering the transmission, or upon receivinga data request from transformative processing engine 108. Eachtransmission can include, e.g., a single record pertaining to a singleentity, object, procedure, or analysis or multiple records pertaining tomultiple entities, objects, procedures, or analyses.

In some examples, at least some of one or more user devices 104 areassociated with facility 110. In some examples, at least some of one ormore user devices 104 need not be associated with facility 110 or anyother facility. Similar to one or more components 102, one or more userdevices 104 may be capable of receiving, generating, processing, and/ortransmitting data. Examples of one or more user devices 104 include, forexample, a computer, a mobile device, a smart phone, a laptop, anelectronic badge, a set-top box, a thin client device, a tablet, apager, and other similar user devices). One or more user devices 104 maybe configured to run one or more applications developed for interactingwith data collected by transformative processing engine 108. Forexample, those user devices of one or more user devices 104 that are notassociated with facility 110 may be configured to run one or morethird-party applications that may rely in part on the data gathered bytransformative processing engine 108.

Each of one or more components 102 and one or more user devices 104 maybe utilized by one or more users (not shown). Each of the one or moreusers may be associated with one or more clients. For example, one ofthe one or more users can be associated with a client as a result ofbeing employed by the client, physically located at a location of theclient, being an agent of the client, or receiving a service from theclient.

In some examples, one or more components 102 and one or more userdevices 104 may communicate with transformative processing engine 108and management engine 106 via different information formats, differentproprietary protocols, different encryption techniques, differentlanguages, different machine languages, and the like. As will bediscussed with reference to FIG. 2, transformative processing engine 108is configured to receive these many different communications from one ormore components 102, and in some examples from one or more user devices104, in their native formats and transform them into any of one or moreformats. The received and/or transformed communications can betransmitted to one or more other devices (e.g., management engine 106,an entity device, and/or a user device) and/or locally or remotelystored. In some examples, transformative processing engine 108 receivesdata in a particular format (e.g., the HL7 format) or conforming to anyother suitable format and/or is configured to transform received data toconform to the particular format.

One or more components 102 of facility 110 can include and/or has accessto a local or remote memory for storing generated data. In someexamples, the data is stored by one or more servers local to facility110. The record service can be granted access to the data generatedand/or transmitted by one or more components 102. In some examples, therecord service includes a server or a plurality of servers arranged in acluster or the like. These server(s) of the record service can processand/or store data generated by one or more components 102. For example,one or more records can be generated for each entity (e.g., each recordcorresponding to a different entity or being shared across entities).Upon receiving a communication with data from a component (or facility),the record service can identify a corresponding record and update therecord to include the data (or processed version thereof). In someexamples, the record service provides data to transformative processingengine 108.

Irrespective of the type of facility, facility 110 may update data,maintain data, and communicate data to transformative processing engine108. At least some of the data may be stored local to facility 110.

A user interacting with a user device 104 can include, for example, aclient customer, client agent and/or a third party. A user may interactwith user device 104 and/or component 102 so as to, for example,facilitate or initiate data collection (e.g., by a component 102),provide data, initiate transmission of a data request, access dataand/or initiate transmission of a data-processing or data-storageinstruction. In some instances, one or more user devices 104 may operateaccording to a private and/or proprietary network or protocols. In otherexamples, one or more user devices 104 may operate on public networks.In any case, however, transformative processing engine 108 can haveaccess to the one or more components and can communicate with them via apublic, private, and/or proprietary network or protocols. The use of oneor more private and/or proprietary protocols can promote secure transferof data.

Referring next to FIG. 2, a block diagram of an example of aninteraction system 200 is shown. Interaction system 200 includes atransformative processing engine 202. Transformative processing engine202 is an example of transformative processing engine 108 discussed withreference to FIG. 1. Interaction system 200 also includes one or moregeneration components 204. In particular, one or more generationcomponents 204 include an equipment component 206, a lab systemscomponent 208, a temporal component 210, and other generation component212. One or more generation components 204 are examples of one or morecomponents 102 discussed with reference to FIG. 1. In some examples, thedata may pass to the transformative processing engine 202 via aninformation exchange service bus 236 (e.g., an enterprise service bus).In some examples, only a portion of the is passed via the informationexchange service bus 236, while other portions are passed directly tothe transformative processing engine 202 without first passing over theinformation exchange service bus 236.

Generally, one or more generation components 204 includes any suitabledevice or system capable of generating data in the context of aninteraction system. For example, the other generation component 212 mayinclude a sensor on a door, and equipment component 206 may include asophisticated computer-controlled laser device. In either case, eachgeneration component generates some type of data. For example, the dataprovided by the sensor may be used to address security concerns orassessing heating, ventilating, and air conditioning (HVAC) costs for aninstitution. The data provided by the laser device may have beenprovided while engaged in a procedure and may then be used by otherentities in the future to decide how to use the device.

As discussed in further detail herein, data generated by one or moregeneration components 204 can be of a variety of formats, some of whichmay be proprietary. For example, a single component can generate data inmultiple formats, different components can generate data in differentformats, and/or different component types can result in generation ofdata in different formats. In some instances, formatting of a data candepend on a service having been provided, a user initiating datageneration, a destination to receive the data, a location at which aservice was provided, etc. In some examples, a typical interactionsystem includes thousands of generation components producing data inhundreds of formats. In order to harness the power that comes from sucha large amount of data to make informed decisions, it is desirable thatall, or at least a large portion of the data, is shared. Use oftransformative processing engine 202 in accordance with techniquesdescribed herein may achieve this design—making large amounts of data,in many different originating formats available to various types ofusers, via one or more interfaces. At least a portion of the datagenerated by the generation components 204 may be provided to thetransformative processing engine 202. In some examples, each generationcomponent 204 includes an agent that executes on the generationcomponents 204 and determines which data to send to the transformativeprocessing engine 202 and other engines described herein. In someexamples, the generation components 204 provide data to thetransformative processing engine 202 via a messaging bus (e.g., aninformation exchange service bus 236). The messaging bus, which may beincluded in the transformative processing engine 202 or separate, isable to see data that moves throughout the interaction system 200. Theinformation exchange service bus 236 also includes a subscriptionregistry that can be used to manage subscriptions to the informationexchange service bus 236 for certain data (e.g., data having certaincharacteristics). The information exchange service bus 236 may sendand/or direct data to certain other entities when appropriate asindicated by subscription records in the registry.

While one or more generation components 204 are illustrated adjacent toeach other, it is understood that each may be located within onefacility or that the components may be spread out among many facilities.In addition, in some examples, one or more generation components 204belong to different clients.

Turning now to equipment component 206, this component includes anymachine, contrivance, implant, or other similar related article, that isintended to aid in reaching a particular objective. In some instances,equipment component 206 includes one or more sensors to detectenvironmental or other stimuli. Equipment component 206 can include, forexample, equipment to monitor a stimulus, detect stimulus changes,detect stimulus-indicative values, and so on. Exemplary equipmentcomponents 206 include an imaging device, a device that detects andcharacterizes electrical signals, a device that detects pressure, and/ora device that detects concentration of one or more particular elements,compounds and/or gases.

As illustrated, equipment component 206 includes transformative adaptor216. In some examples, transformative adaptor 216 is a device thattransforms, translates, converts, or otherwise adjusts output data fromequipment component 206. For example, an equipment component 206 can bea scanner that outputs its results in format A, but the majority ofother scanners in the interaction system output their results in formatB. Transformative adaptor 216 may be implemented to convert or otherwiseadjust the results in format A to conform closer to format B. Forexample, the conversion from format A to format B may be performed usinga conversion rule, which may be user-define or learned. Transformativeprocessing engine 202 may perform similar tasks as it relates to alldata generated within interaction system 200. In this manner,transformative adaptor 216 can perform an initial step in the process oftransformation, translation, conversion, or adjustment of the output ofequipment component 206. In some examples, transformative adaptor 216 isimplemented in hardware, software, or any suitable combination of both.In some examples, other transformative adaptors (not shown) may beimplemented within others of one or more generation components 204. Insome examples, equipment component 206 may not include transformativeadaptor 216.

Lab systems component 208 includes any suitable laboratory equipment orsystem that is intended to analyze material, such as biologicalmaterial. This includes, for example, laboratory equipment that analyzesbiological samples; electric microscopes; ultracentrifuges; datacollection devices, including Kymographs, sensors connected to acomputer to collect data; monitoring devices; computers used to reportresults of lab tests, and other similar laboratory equipment. Each ofthe above-listed components generates data that is provided (directly orindirectly) to transformative processing engine 202.

Temporal component 210 may include any suitable computing devices usedwith respect to interaction system 200. For example, temporal component210 can be configured to allocate a resource to a particular entityduring a particular temporal window. Temporal component 210 can monitora schedule for the resource and can identify one or more availabletemporal windows that may be secured by a particular entity. Uponreceiving an indication, temporal component 210 may update a schedule ofa resource to reflect that a particular temporal window is to beallocated for service of a particular entity.

Each of one or more generation components 204 and the user device 228may include individual and/or shared storage systems, one or moreprocessors, a user interface, a network connectivity device, and one ormore ports. The storage system include memory that may be implemented,e.g., using magnetic storage media, flash memory, other semiconductormemory (e.g., DRAM, SRAM), or any other non-transitory storage medium,or a combination of media, and can include volatile and/or non-volatilemedia. The storage systems may also be configured to storecomputer-executable code or instructions for interacting with the userinterface and/or for one or more applications programs, such as anapplication program for collecting data generated by the particulargeneration component.

The one or more processors may be configured to access the operatingsystem and application programs stored within the storage systems, andmay also be configured to execute such program code. The one or moreprocessors can be implemented as one or more integrated circuits, e.g.,one or more single-core or multi-core microprocessors ormicrocontrollers, examples of which are known in the art. In operation,the one or more processors can control the operation of the particularcomponent. The one or more processors may access and execute the programcode and at any given time.

The user interface can include any combination of input and outputdevices. In some instances, a user can operate input devices of the userinterface to invoke the functionality of the particular component oruser device. For example, the user interface may enable the user toview, hear, and/or otherwise experience output from component or userdevice via the output devices of the user interface. Examples of outputdevices include a display, speakers, and the like.

The network connectivity device may enable the component or user deviceto communicate with transformative processing engine 202 and othercomponents or other user devices via one or more networks. The one ormore networks may include any suitable combination of cable, cellular,radio, digital subscriber line, or any other suitable network, which maybe wired and/or wireless. In some examples, the network connectivitydevice may enable the component or the user device to communicatewirelessly with various other components and/or transformativeprocessing engine 202. For example, the components may include circuitryto enable data communication over a wireless medium, e.g., usingnear-field communication (NFC), Bluetooth Low Energy, Bluetooth® (afamily of standards promulgated by Bluetooth SIG, Inc.), Zigbee, Wi-Fi(IEEE 802.11 family standards), or other protocols for wireless datacommunication.

The one or more ports may enable the component or the user device toreceive data from one or more sensors. The sensors may be any suitabletype of sensor to capture data. Such captured data may be shared withtransformative processing engine 202 in accordance with techniquesdescribed herein. In some examples, the sensors may also be configuredto detect the location and other details about the component or the userdevice. In some examples, the component and the user device may includeglobal positioning chips that are configured to determine a geolocation.

Transformative processing engine 202 includes an aggregation engine 218,an interoperability engine 220, an access management engine 222, aninterface engine 224, and a data store 226. Generally aggregation engine218 is configured to collect data from multiple communications. The datamay be from one or multiple generation components 204 and/or may be ofsame or different formats. Aggregation engine 218 may be configured toperform one or more operations on the collected data. For example,aggregation engine 218 may tag data, log data, perform protocolconversion, and may support one-to-many communications. The collectionmay be asynchronous. In some examples, the data has been saved locallyin connection with one or more generation components 204 in manydifferent formats having many different data structures.

Aggregation engine 218 can identify data to be aggregated based on, forexample, intra-communication data, a current time, a source generationcomponent, and/or one or more aggregation rules. For example, anaggregation rule may specify that data is to be aggregated across allcommunications that include content with a same entity identifier. Anaggregation may be dynamic. For example, aggregated data may reflectthat from within a most recent 12-hour period. Thus, an aggregation maybe updated in time to exclude older data from the aggregation and toinclude newer data.

Aggregation engine 218 can be configured to provide data from one ormore communications to interoperability engine 220. Interoperabilityengine 220 can be configured to perform one or more operations on thereceived data and store it in data store 226. For example,interoperability engine 220 may perform semantic tagging and indexing ofdata. This may include extracting field values from data, categorizingdata (e.g., by type of data, characteristic of an entity, location offacility, characteristic of facility, and the like), anonymizing orpartially-anonymizing data, and the like. Interoperability engine 220may also include a high availability cache, an alerts engine, and arules engine. In some examples, interoperability engine 220 operatessynchronously.

From interoperability engine 220, data flows to data store 226. Datastore 226 (and any other data store discussed herein) may include one ormore data stores, which may be distributed throughout two or moredifferent locations (e.g., present on different devices, which caninclude devices of different entities and/or a cloud server). In someexamples, data store 226 includes a general data store 230, anoperational data store 232, and an entity-based data store 234. Withineach of the data stores 230, 232, and 234 is stored data. Depending onthe structure of the particular data store, certain data stores mayinclude rules for reading and writing. The data stores 230, 232, and 234may include records, tables, arrays, and the like, which may berelational or non-relational. Depending on the data store, records forindividual entities, business and analytics information, output datafrom one or more generation components 204, and the like may beretained. The data within the data stores 230, 232, and 234 includeelements or tags such that a particular data (e.g., for a single entity,protocol, etc.) can be retrieved.

Access management engine 222 is configured to manage access to featuresof transformative processing engine 202, including access to the dataretained in data store 226. For example, access management engine 222may verify that a user device such as user device 228 is authorized toaccess data store 226. To verify the user device 228, access managementengine 222 may require that a user of the user device 228 input ausername and password, have a profile associated with the interactionsystem, and the like. Access management engine 222 may also verify thatthe user device 228 has an IP address or geographical location thatcorresponds to an authorized list, that the user device 228 includes aplug-in for properly accessing the data store 226, that the user device228 is running certain applications required to access the data store226, and the like.

Interface engine 224 is configured to retrieve the data from data store226 and provide one or more interfaces for interacting with elements oftransformative processing engine 202. For example, interface engine 224includes an interface by which an application running on user device 228can access portions of data within data store 226.

As described herein, an information exchange engine 238 shares a networkconnection with the information exchange service bus 236. Theinformation exchange engine 238 is configured to monitor data (e.g.,messages) that is passed over the information exchange service bus 236and, from the monitored data, select certain portions to provide to oneor more authorized user devices. The information exchange engine 238 isalso configured to route inbound messages and route outbound messages,as described herein. The information exchange engine 238 is alsoconfigured to generate customized messages based on dependent user data.

Turning next to FIG. 3, an architecture stack 300 is shown. In someexamples, techniques relating management of data are implemented inaccordance with architecture stack 300. And while architecture stack 300is illustrated as having a particular structure, it is understood thatother structures, including those with more or less layers thanillustrated, is within the scope of this specification. In someexamples, architecture stack 300 is implemented across an interactionsystem having a plurality of systems belonging to the same client orspread across different clients. Thus, architecture stack 300 can beused to integrate different systems of different organizations,entities, and the like and to provide a fluid sharing of informationamong elements within the interaction system and without the interactionsystem. In some instances, a multi-layer part of architecture stack 300is implemented at a single system or device within an interactionsystem.

The different layers of architecture stack 300 will be describedgenerally with reference to FIG. 3 and in detail with reference tosubsequent figures. Architecture stack 300 includes a receiving layer302 as the bottom-most layer. Receiving layer 302 includes receivingdata from elements that share data with other elements within anaggregation layer 304. For example, as detailed herein, receiving layer302 can include receiving data from generation components that generatedata. As such, receiving layer 302 is where data that has been createdis received. In some examples, the data within receiving layer 302 maybe in its raw formats. The output may then be transmitted to aggregationlayer 304. In some examples, components of receiving layer 302 may havecomplimentary layers to facilitate data transfer. For example, thecomponents may include a data generation and/or a data transmissionlayer for providing data to receiving layer 302.

Elements of aggregation layer 304 aggregate the data generated by theelements of receiving layer 302. For example, the elements ofaggregation layer 304 may include aggregation engines that collect datafrom generation components located within receiving layer 302. Suchaggregation may be performed periodically, in response to a userrequest, according to a schedule, or in any other suitable manner. Insome examples, data of aggregation layer 304 may be aggregated accordingto input and/or rules and may aggregate across records pertaining to,e.g., a facility, entity, time period, characteristic (e.g., demographiccharacteristic or condition), outcome, and any other suitable inputand/or rules. The aggregation may include compiling the data, generatinga distribution, generating a statistic pertaining to the data (e.g.,average, median, extremum, or variance), converting the data,transforming the data to different formats, and the like.

Next, architecture stack 300 includes an active unified data layer 308.Elements of active unified data layer 308 receive data from the elementsof the other layers and store such data in a unified manner. In someexamples, this may include storing the data in a manner that allows forlater searching and retrieval using a defined set of method calls,techniques, and or procedures. For example, the data may be stored suchthat a different application can access the data in a standard orunified manner. Thus, elements of active unified data layer 308 mayreceive information collected or generated within aggregation layer 304and make certain adjustments to the data (e.g., translations, tagging,indexing, creation of rules for accessing the data, conversion offormatting of the data, generation of compressed versions, and the like)prior to retaining the data within one or more data stores accessiblewithin active unified data layer 308.

Architecture stack 300 also includes an access management layer 310,which can include an audit/compliance layer 312 and/or an agency layer314. Access management layer 310 includes elements to manage access tothe data. For example, access management layer 310 may include elementsto verify user login credentials, IP addresses associated with a userdevice, and the like prior to granting the user access to data storedwithin active unified data layer 308.

Audit/compliance layer 312 includes elements to audit other elements ofarchitecture stack 300 and ensure compliance with operating procedures.For example, this may include tracking and monitoring the other elementsof access management layer 310.

Agency layer 314 includes an access location (e.g., a virtual privatenetwork, a data feed, or the like) for elements of agencies that areinterested in the operations of the interaction system in whicharchitecture stack 300 is implemented. For example, agency layer 314 mayallow a governmental entity access to some elements within architecturestack 300. This may be achieved by providing the governmental entity adirect conduit (perhaps by a virtual private network) to the elements ofaccess management layer 310 and the data within active unified datalayer 308. Audit/compliance layer 312 and agency layer 314 aresub-layers of access management layer 310.

Architecture stack 300 also includes interface layer 316. Interfacelayer 316 provides interfaces for users to interact with the otherelements of architecture stack 300. For example, clients, entities,administrators, and others belonging to the interaction system mayutilize one or more user devices (interacting within application/devicelayer 320) to access the data stored within active unified data layer308. In some examples, the users may be unrelated to the interactionsystem (e.g., ordinary users, research universities, for profit andnon-profit research organizations, organizations, and the like) and mayuse applications (not shown) to access the elements within architecturestack 300 via one or more interfaces (e.g., to access data stored withinactive unified data layer 308). Such applications may have beendeveloped by the interaction system or by third-parties.

Finally, architecture stack 300 includes application/device layer 320.Application/device layer 320 includes user devices and applications forinteracting with the other elements of architecture stack 300 via theelements of interface layer 316. For example, the applications may beweb-based applications, entity portals, mobile applications, widgets,and the like for accessing the data. These applications may run on oneor more user devices. The user devices may be any suitable user deviceas detailed herein.

Turning next to FIG. 4, a diagram 400 is shown that depicts a portion ofarchitecture stack 300 according to at least one example. In particular,the diagram 400 includes receiving layer 302, aggregation layer 304,aggregation layer 306, and a portion of active unified data layer 308.Receiving layer 302 receives data from one or more components 410-418.Components 410-418 are examples of one or more generation components204. Components 410-418 may be spread across multiple facilities withina single or multiple clients. In some examples, components 410-418 mayinclude complimentary layers to facilitate data transmission. Forexample, components 410-418 may include a transmission layer, generationlayer, and/or a receiving layer to communicate data at receiving layer302 and, in some examples, receive data from receiving layer 302.

In some instances, two or more of components 410-418 generate dataaccording to different formats. The data can then be transformed,translated, or otherwise adjusted before an aggregation engine 420(e.g., aggregation engine 218) or a third-party aggregation engine 422(e.g., aggregation engine 218) collects the data. In some examples, theadjustment takes place within receiving layer 302. Thus, an adaptor 424is associated with component 412 located in receiving layer 302. Adaptor424 is an example of transformative adaptor 216. Adaptor 424 isimplemented, as appropriate, in hardware, software, or any suitablecombination of both. For example, transformative adaptor 216 may be abolt-on adaptor that adjusts data as such data leaves component 412.

Other adaptors, such as adaptor 426 and adaptor 428, are implementedwithin aggregation layer 304. These adaptors can function in a similarmanner as adaptor 424. In some examples, the data provided by component414 is transmitted through adaptor 426 prior to being directed toaggregation engine 420. The data provided by component 416 istransmitted through aggregation layer 304 and/or enters aggregationengine 420 without having first traveled through an adaptor. The dataprovided by component 418 is transmitted through aggregation layer 304and through adaptor 428. In some examples, component 418 provides forstreaming of data. The data provided by component 410 is transmitteddirectly to third-party aggregation engine 422.

Aggregation engine 420 and third-party aggregation engine 422 functionin a similar manner. In some examples, third-party aggregation engine422 is operated by a different entity than the entity that operatesaggregation engine 420 and may belong to different clients or adifferent interaction system. This may be because the data collected bythird-party aggregation engine 422 differs in some way from the datacollected by aggregation engine 420. In any event, aggregation engine420 is configured to perform integration of data, including genericintegration. For example, aggregation engine 420 performs one or moreoperations on data including tagging, logging, and protocol conversion.Aggregation engine 420 also supports one-to-many communications of data.In some examples, data flows between aggregation engine 420, thethird-party aggregation engine 422, and some of components 410-418 andelements of active unified data layer 308.

The diagram 400 also includes the information exchange service bus 236and the information exchange engine 238. As introduced herein, messagespassing through the aggregation layer 304 can pass over the informationexchange service bus 236. In this manner, the information exchangeengine 238 can access the messages, route the messages, and/or customizethe messages.

Referring next to FIG. 5, a diagram 500 is shown that depicts a portionof architecture stack 300 according to at least one example. Inparticular, diagram 500 includes active unified data layer 308 and aportion of access management layer 310. Active unified data layer 308,as illustrated in diagram 500, includes an interoperability engine 502(e.g., interoperability engine 220), a collection engine 504, a datastore integrity engine 506, and a data store 508 (e.g., data store 226).Generally, interoperability engine 502 receives data from elementswithin aggregation layer 304 (e.g., from aggregation engine 420) andperforms one or more operations with respect to the data.Interoperability engine 502 also facilitates storage of at least aportion of the processed information in data store 508.

Collection engine 504 is configured to generate message indicatorsidentifying flows of data by and between elements of an interactionsystem implemented using the techniques described herein. The flows ofinformation include messages which include data, and the messageindicators include unique message identifiers that can be used toidentify the messages. The unique message identifiers includeinformation that can be used to uniquely identify the messages. Forexample, a unique message identifier for a particular message caninclude a concatenation of the following information stored in a table:a source application, a facility, a message type, and a message controlidentification (ID). The unique message identifier can also be themessage control ID. The unique message identifier may be created asmessages including data are transmitted from aggregation layer 304.

In some examples, the table also includes information for tracking theprogress of the message from an origination node to a destination node.For example, typically when a message (e.g., any communication of data)is first received by transformative processing engine 108 (e.g.,interoperability engine 502), management engine 106 (e.g., collectionengine 504 of management engine 106) may generate a unique identifierfor the message in order to track that message as it moves throughoutthe interaction system. The unique identifier may be included in theheader of the message such that when the next node (e.g., component,device, server, etc.) after transformative processing engine 108receives the message, that node can report back to management engine 106that it saw the message. In this manner, management engine 106 may trackmessages from end-to-end for the life of the message.

In one example, the messages are requests. The requests may be generatedbased om user input at one of the components. The requests may bereceived by transformative processing engine 108 and integrated into thesystem. In some examples, management engine 106 may be notified that therequests have been received and may therefore be configured to generatemessage IDs for each request. These message IDs may then be associatedwith each of the requests. As the requests continue to move throughoutthe interaction system (e.g., away from transformative processing engine108), management engine 106 may track their movement using the messageIDs. If one of the requests does not arrive at its destination,management engine 106 may determine why the request was stopped. In someexamples, this cause may be hardware related (e.g., an unpluggedEthernet cable, a broken router, etc.), software related (e.g., a routerrouting to the wrong location), or any other reason for orders notarriving at their correct destination.

In some examples, management engine 106 (e.g., collection engine 504 ofmanagement engine 106) may receive the message and/or message identifierdirectly from one of components 410-418. For example, one of components410-416 may be configured to generate the unique message identifierand/or communicate directly with management engine 106. The message alsomay travel via one or more intermediate nodes on its way to thedestination node. In some examples, a node is a component such ascomponents 410-418, which may be running an application. In someexamples, the unique identifier and the routing of the message to itsdestination may be stored in a table that also includes: a geolocationof each node, a network from which the message originated, a type ofnode, the unique node identifier, and a time associated with the messageleaving the origination node. In some examples, collection engine 504provides unique message identifiers to other elements of the interactionsystem to monitor the messages as they move throughout the interactionsystem. Collection engine 504 also provides a portion of the uniquemessage identifiers to a management platform (indicated by a circle 528)for further analysis of the message identifiers. Such analyses mayinclude reconciliation of lost messages, latency reporting, auditmanagement and compliance, and other such analyses.

As mentioned previously, interoperability engine 502 is configured tostore data in data store 508. A plurality of sub-engines 510-516 ofinteroperability engine 502 are configured to perform operationsrelating to storing data in data store 508.

Interoperability engine 502 includes a tagging engine 510 configured toperform semantic tagging and indexing of data. Tagging engine 510therefore is configured to receive data, read metadata associated withthe data, semantically scan the content of the data, and associate oneor more tags with the data. Tagging engine 510 may therefore have accessto hundreds, thousands, or even more possible tags. These tags may havebeen input by users, learned, pre-defined, generated by outsidethird-party mapping sources, and/or gathered from other componentsand/or data stores of the interaction system. For example, if the datais a chart for an entity, the tagging engine may be configured to readany metadata associated with the chart to determine which tags may beappropriate to associate with the chart. From the metadata, taggingengine 510 may determine that the chart is for a type of entity byreading metadata indicating that an author field is populated with thename of another particular type of entity. Tagging engine 510 may haveaccess to other data to compare the analyzed metadata against (e.g., toidentify that the author's name corresponds to Dr. Brown who is anoncologist). Other examples, of metadata that may be included in one ormore fields include author, document type, creation time and date, lastupdate time and date, upload time and data, geographic location, uniqueID associated with the client or facility where the data originated, andother similar fields. The tags may be stored in association with thedata (e.g., the chart) and/or may be stored independent from the databut include an identifier such that when searching tags the data may becapable of population.

Continuing with the example from above, if the data is a chart for afirst type of entity, tagging engine 510 may be configured to read thecontent of the chart to determine which tags may be appropriate toassociate with the chart. For example, this may comprise analyzing thecontent of the chart (i.e., individual pages) semantically to look forartifacts (e.g., keywords, phrases, and the like) in the content. Theseartifacts may be identified by tagging engine 510 and used to decidewhich tags to associate with the document. In some examples, semanticscanning may involve filtering out words (e.g., articles, such as “a”and “the”), phrases, and the like. Similar to the reading of metadata,the tags may be pre-defined, user-defined, learned, and the like. Insome examples, reading metadata associated with messages may providemeaning and/or give context to the particular record of data. Thismeaning and/or context may assist tagging engine 510 to determine one ormore tags to associate with the data. The tags may be chosen, forexample, based on values of particular fields in the data, detecting afrequency of one or more words in a document or metadata and/or of a setof related words (e.g., tagging a record with “cancer” upon detectingwords such as tumor, metastasize, chemotherapy, radiation, oncology,malignant, stage 3, etc.). In this manner, tagging engine 510 may alsoindex portions of the data within one or more data stores of data store508. In some examples, such indexing may be based in part on theselected tags.

Interoperability engine 502 also includes a reports engine 512configured to generate one or more reports or alerts based on data. Forexample, reports engine 512 may generate reports when certain types ofdata are received or when data with certain characteristics is received.Reports engine 512 may also generate alerts. The reports and/or alertsgenerated by reports engine 512 may be outputted in the form of one ormore communications to an administrator, an authorized user, or othersimilar user via a user device. Such communications can include, forexample, signals, sirens, electronic notifications, popups, emails, andthe like. Content of such communications may include informationcharacterizing a performance metric, efficiency and/or outcomes;identifying concerning patterns; identifying losses of data; and thelike. In some examples, the content is presented in the form of one ormore documents, tables, figures, charts, graphs, and the like.

Interoperability engine 502 also includes a rules engine 514 configuredto create and manage condition-response rules, alert/reports rules,data-formatting rules, data-sharing rules, transmission rules,aggregation rules, user authorization rules, and other similar rules.Such rules may be user-defined, fixed, learned by elements of theinteraction system, and any combination of the foregoing. Finally,interoperability engine 502 includes an application engine 516configured to provide service-oriented architecture web services.

Data store 508 includes an electronic record information data store 518(“ERI data store 518”), a general data store 520, an operational datastore 522, an entity-based data store 524, and a streaming cachingstorage 526. While data store 508 is illustrated as including a fixednumber of data stores and storage elements, it is understood that datastore 508 can include any suitable number of data stores and storageelements, including more than illustrated or less than illustrated.

In some examples, a data query script is provided to query a first datastore and/or to obtain data for populating a data store. Such scriptcould query a data store described herein (e.g., data store 508) and/orcould be used to obtain data to populate a data store described herein(e.g., data store 508). In one instance, the script is configured to berepeatedly executed, so as to repeatedly draw data from a source datastore. The retrieved data can then be formatted, filtered, sorted and/orprocessed and then stored, presented and/or otherwise used. In thismanner, the script can be used to produce streaming analytics.

In some instances, the data query script, when executed, identifies eachof the data stores of interest. Identifying the data stores of interestinvolves identifying at least a portion of data from the data storessimultaneously and/or sequentially. For example, the script can identifycorresponding data stores (e.g., or components of a single data store ormultiple data stores) that pertain to one or more similar variables butthat differ in one or more other variables. Once the portion of the datafrom the data stores is identified, a representation of the identifieddata can be output to one or more files (e.g., Extensible MarkupLanguage (XML) files) and/or in one or more formats. Such outputs canthen be used to access the data within one or more relational databaseaccessible using Structured Query Language (SQL). Queries made using SQLcan be made sequentially or in parallel. Results from an SQL query maybe stored in a separate database or in an XML file that may be updatedeither in part or as a whole. The data query script may be executedperiodically, in accordance with a user-defined rule, in accordance witha machine-defined or machine-learned rule, and in other suitable manner.

Within ERI record data store 518 is retained data. In some examples, theinformation within ERI record data store 518 is organized according toentity identifying information. Thus, ERI record data store 518, in someexamples, includes individually identifiable information. But it mayalso include de-identified information.

Within general data store 520 is retained data. The data may be storedin a relational database format or in any other suitable format. Thus,the data within general data store 520 may be retained in a datastructure that includes one or more tables capable of accessing eachother. In some examples, general data store 520 includes a subset of theinformation that is included in operational data store 522.

Within operational data store 522 is retained data in a relationaldatabase format. Thus, the data within operational data store 522 may beretained in a data structure that includes one or more data structures(e.g., tables) capable of accessing each other. Operational data store522 is an example of an operational data warehouse. In operational datastore 522 is joined many different types of data. In some examples, theoperational data store 522 includes data pertaining to decision makingas discussed herein and other data typically used.

Within entity-based data store 524 is retained data in a non-relationaldatabase format. Thus, the data within entity-based data store 524 maybe retained in a structure other than tables. Such structure may beappropriate for large and complex data sets. In some examples,entity-based data store 524 (or any other data store) may be a unifiedsystem, which may include: a document-centric, schema-agnostic,structure-aware, clustered, transactional, secure, database server withbuilt-in search and a full suite of application services. An example ofsuch a unified system may be Marklogic. Entity-based data store 524 cansupport data aggregation, data organization, data indexing, data taggingand mapping to semantic standards, concept matching, concept extraction,machine learning algorithms, concept discovery, concept mining, andtransformation of record information. In some examples, entity-baseddata store 524 includes data pertaining to decision making (similar togeneral data store 520) as discussed that is organized and accessed in adifferent manner. For example, the data within entity-based data store524 may be optimized for providing and receiving information over one ormore information exchanges. In some examples, entity-based data store524 includes a subset of the information that is included in operationaldata store 522.

Finally, in some examples, streaming caching storage 526 is a streamingdata cache data store. As discussed previously, certain components ofcomponents 410-418 may support streaming data to other components oruser devices. Streaming caching storage 526 is a location wherestreaming data can be cached. For example, assume that component 418 isa piece of equipment operating at Location A and that a user using acomputer in Location B desires to view a live of substantially livestream of outputs of the piece of equipment. Component 418 can send aportion of data to streaming caching storage 526 which can retain theportion of the data for a certain period of time (e.g., 1 day). Thus,streaming caching storage 526 is configured to cache data that can bestreamed.

Diagram 500 also includes data store integrity engine 506. In someexamples, data store integrity engine 506 is configured to ensureintegrity of the information within data store 508. For example, datastore integrity engine 506 applies one or more rules to decide whetherinformation within all or part of data store 508 should be scrubbed,removed, or adjusted. In this manner, confidence is increased that theinformation within data store 508 is accurate and current.

FIG. 6 shows a diagram 600 which depicts a portion of architecture stack300 according to at least one example. In particular, the diagram 600includes access management layer 310, audit/compliance layer 312, agencylayer 314, and a portion of interface layer 316.

Access management layer 310, as illustrated in the diagram 600, includesan access management engine 602. Access management engine 602 is anexample of access management engine 222. Generally, access managementengine 602 can be configured to manage access to elements oftransformative processing engine 202 by different components,applications, and user devices.

Access management engine 602 within access management layer 310 alsoprovides functionality similar to an operating system. For example,access management engine 602 includes a plurality of engines configuredto manage different aspects of interacting with elements of theinteraction system. For example, a user who desires to access portionsof data retained in data store 508, may do so by interacting with accessmanagement engine 602 using one or more applications (not shown). Thus,access management engine 602 includes a variety of engines to enablesuch interaction. The engines include, for example, an authenticationaccess engine 604, a login engine 606, a user preference engine 608, asecurity engine 610, an analytics and search engine 612, a data accessengine 614, an update engine 616, and a streaming data engine 618. Thedifferent engines of access management engine 602 can define routines,protocols, standards, and the like for interacting with elements of theinteraction system.

Beginning first with authentication access engine 604, authenticationaccess engine 604 evaluates the rules and conditions under which usersmay access elements of the interaction system; in particular, theconditions under which users may access data within data store 508.These rules and conditions may be user-defined (e.g., by anadministrator or reviewer), learned over time, and/or may be dynamicallyupdated and/or evaluated based on characteristics of the user or theuser's device attempting to access the interaction system. The rules andconditions may indicate the types of users who have particular types ofaccess within the interaction system. The type of access may also relateto the degree to which data is identified/de-identified. In someexamples, a user desiring access to data provides certain identifyinginformation and authentication access engine 604 authenticates anidentity of the user.

Login engine 606 evaluates the rules and conditions under which usersare able to log in to the interaction system or access applicationsassociated with the interaction system. These rules and conditions maybe user-defined (e.g., by an administrator), learned over time, and alsomay be dynamically updated and/or evaluated based on characteristics ofthe user or the user's device attempting to access the interactionsystem. Thus, while authentication access engine 604 evaluates the rulesto determine which users may access the interaction system, login engine606 evaluates the particular credentials, profiles, etc. of the users.For example, login engine 606 can confirm that an entered username(e.g., and password), provided biometric data or code or identifier in ascanned tag or badge matches that in an authorized user data structure.

Login engine 606 evaluates one or more user profiles associated witheach authenticated user. In some examples, a user profile includes ausername, password, and other information associated with the user. Forexample, a user profile may indicate characteristics about the user.

User preference engine 608 evaluates the rules and conditions underwhich user are able to store and update one or more user preferencescorresponding to access of the interaction system or access toapplications associated with the interaction system. These rules andconditions may be user-defined (e.g., by the user or administrator), andmay include rules for default preferences. For example, using userpreference engine 608, a user may indicate a format in which the userprefers to receive outputted information, display characteristics of agraphical user interface associated with the user, and other similaruser preference settings. For example, the user may indicate thatcertain types of reports and/or alerts are to be sent to the user.

Security engine 610 evaluates the rules and conditions for ensuring thesecurity of access to the elements of the interaction system. In someexamples, these rules and conditions are determined by administrators ofthe interaction system. In some examples, security engine 610 provides aplurality of computer virus protection services. These services can becalled up and implemented when accessing the interaction system oraccessing applications associated with the interaction system. The rulesand conditions may be based on roles, based on profiles, based ondomains, and any other suitable security configuration. For example,because the interaction system may include sensitive data, securityengine 610 may enforce a domain-based rule that protects certainsensitive information (e.g., identifying information).

Analytics and search engine 612 evaluates the rules and conditions underwhich users can search for data within the interaction system and accessanalytics relating to the interaction system. In some examples, theserules and conditions are user-defined or learned over time in accordancewith search engine optimization techniques. For example, analytics andsearch engine 612 is used to search within data store 508 for particulardata. Analytics and search engine 612 supports any conventionalsearching algorithms. For example, search engine 612 can be used tosearch within various fields and potential field values. In someexamples, search engine 612 can provide analytics, such as statistics,graphs, distributions, and/or comparative analysis pertaining toparticular entities and/or characteristics. Such information may beselected by a user and presented on a user interface.

Data access engine 614 evaluates the rules and conditions under whichusers may operation in order to access particular data within data store508. In some examples, these rules and conditions are user-defined orlearned over time. For example, data access engine 614 may indicate theroutines, subroutines, or other logic needed for an application toaccess certain portions of data store 508. For example, whileauthentication access engine 604 and login engine 606 may manage whichusers can access parts of the interaction system, data access engine 614may manage how authenticated users access data within data store 508. Tothis end, data access engine 614 may enforce and/or evaluate certainrules managing how users access different components of the interactionsystem. In some examples, data access engine 614 may be used to actuallyaccess data within data store 508 (e.g., extract, download, or otherwiseaccess). In some examples, data access engine 614 may define procedures,protocols, and the like for accessing data. The protocols and proceduresfor accessing data access engine 614 (like the other engines of accessmanagement engine 602) may be provided to developers in the form of asoftware development kit (SDK). SDKs may enable developers writeapplications that can effectively communicate with elements (e.g., datastore 508) of the interaction system. In particular, applications thatcan access a portion of the data stored within active unified data layer308.

Update engine 616 evaluates the rules and conditions for providingupdates to other engines within access management engine 602, plug-insfor applications that access the interaction system, and for othersimilar elements of the interaction system. For example, updates may begenerated at runtimes, at defined time intervals, upon request by auser, upon receiving a threshold quantity of new or changed data. Oncean update is performed, an interface may be refreshed, a report may besent indicating that the update was successful or unsuccessful, or thelike.

Streaming data engine 618 defines the rules and conditions for enablingstreaming of data between components and user devices of the interactionsystem. For example, streaming data engine 618 may enable component 414to stream data. Streamed data may include live or substantially liveaudio or video feeds, results of tests, output from equipment ordevices, and any other suitable type of data capable of being streamed.In some examples, the data may be streamed to other components or userdevices within the network or outside the network. In order to establisha streaming transmission, streaming data engine 618 may identify astreaming destination and a streaming origin. Next, streaming dataengine 618 may pair the two and enable streaming. This may includeallocated bandwidth within one or more network devices associated withthe interaction system. Streaming data engine 618 may also adjust thequality of the streaming data based on the availability of bandwidth. Insome examples, streaming data engine 618 may receive incoming streams(and continuously present the stream or monitor for particular data(e.g., exceeding a threshold, exhibiting an above-threshold change,having a particular value)).

Within audit/compliance layer 312 is located an access log engine 622.Access log engine 622 evaluates the rules and conditions for loggingaccess to the interaction system by users, applications, devices, andthe like. Logging access includes, in some examples, logging dataconventionally collected by access log engines running in similarenvironments. Access log engine 622 can use this data to generate andtransmit reports, for example, to stakeholders of the interaction systemsuch that they can make informed decisions regarding that is accessingthe interaction system and for what purposes.

Within agency layer 314 is located an agency engine 624. Agency engine624 evaluates the rules and conditions under which agencies can accessthe interaction system. In some examples, agency engine 624 may be usedto track one or more performance indicators identified by a governmentagency and/or to provide report instances of defined types of events. Insome examples, a university is an agency that uses agency engine 624 tocollect data pertaining to one or more studies. Agency engine 624 cancollect the pertinent data, potentially format and/or analyze the data,and facilitate transmission of the data to the appropriate agency.

FIG. 7 shows a diagram 700 which depicts a portion of architecture stack300 according to at least one example. In particular, diagram 700includes interface layer 316, and application/device layer 320. Withininterface layer 316 is located interface engine 702 (e.g., interfaceengine 224). Interface engine 702 is configured to generate one or moreinterfaces (e.g., graphical user interface 726, programmatic interface728, and/or web interface 730) to enable data to flow to user devices710, 712, and 714 via respective applications 720, 722, and 724. In someexamples, the interfaces of interface engine 702 are embodied inhardware, software, or some combination of both. Within interface layer316 communications and inputs directed to interacting with elements ofaccess management layer 310 may be embodied.

Graphical user interface 726 is any suitable graphical user interfaceconfigured to interact with elements of the interaction system.Programmatic interface 728 includes an application programminginterface, a programmatic user interface, and other similar interfacesfor defining core functions for accessing elements of the interactionsystem. For example, programmatic interface 728 may specify softwarecomponents in terms of their operations. Web interface 730 is anysuitable web interface configured to interact with elements of theinteraction system. Any of the interfaces described herein may beconfigured to receive user input, present dynamic presentations thatdepend on user input, and otherwise respond to user input. In someexamples, such input may be provided via one or more input devices(e.g., a keyboard, touchscreen, joystick, mouse, microphone, devicescapable of capturing inputs, and the like) operated by one or more usersof user devices 706-714. Output may be provided via one or more outputdevices (e.g., a display or speaker).

Interface engine 702 is utilized by applications internal to theinteraction system and external to the interaction system to accessdata. In some examples, the applications that are internal includeapplications that are developed for internal use by various entitiesassociated with the interaction system. In some examples, theapplications that are external to the interaction system includeapplications that are developed for external use by those that are notassociated with the interaction system.

Generally, within application/device layer 320, applications 716-724which communicate with other elements of architecture stack 300 usingthe interfaces generated by interface engine 702 are defined. Thisincludes detailing how applications 716-724 are to interact with theinterfaces generated by interface engine 702 for accessing data. Forexample, interacting may include accepting inputs at user devices706-714 to access data and, in response, providing the data, prompts, orother types of interaction with one or more users of the user devices706-714. Thus, applications 716-724 may be related to one or more of theinterfaces generated by interface engine 702. For example, application720 may be interact with a graphical user interface (whether generatedby interface engine 702 or otherwise) to interact with other elements ofthe interaction system. Interacting may include receiving inputs at thegraphical user interface via application 720, providing output data tothe graphical user interface application 720, enabling interaction withother user devices, other applications, and other elements of theinteraction system, and the like. For example, some of the inputs maypertain to aggregation of data. These inputs may include, for example,types of data to aggregate, aggregation parameters, filters ofinterested data, keywords of interested data, selections of particulardata, inputs relating to presentation of the data on the graphical userinterface, and the like. Providing output data may include providing theaggregated data on the graphical user interface, outputting theinformation to one of the other user devices 706-714 running one of theother applications 716-724.

Turning now to the details of applications 720, 722, and 724. In someexamples, applications 720, 722, and 724 include a variety of differentapplications that can be designed for particular users and/or uses. Inone example, application 720 includes dashboards, widgets, windows,icons, and the like that are customized for a particular entity. In someexamples, application 720 may present different data depending on afocus of the entity and protected information associated with theentity. In this manner, application 720 adapts and automatically adjustsdepending on the context in which the entity is using the application.Application 720 may be configured to receive input, adjustpresentations, present unprompted alerts, adjust display of content,move more relevant content to the foreground, move less relevant contentto the background, and/or populate forms for the entity.

In another example, application 722 may be specific for nurses or typesof nurses. In this example, application 722 may include dashboards,widgets, windows, icons, and the like that are customized to individualnurses. Similar to the example discussed above pertaining to the user,in some examples, application 724 may present different data dependingon a position of the nurse. In this manner, application 722 adapts andautomatically adjusts depending on the context in which the nurse isusing the application. For example, the nurse may receive data, such astest results.

In some examples, application 724 may be a multi-role application foradministrators and is used to manage entities constitute the populationof the entities or organizations within the interaction system. Similarto the other examples discussed, in some examples, application 724 maypresent different data depending on a role of the user who is usingapplication 724. In this manner, application 724 adapts andautomatically adjusts depending on characteristics of the user who isusing application 724. In this manner, application 724 can providedifferent data depending on the role of the user. For example, whetherdata presented includes identifiable or de-identified information maydepend on a position of the user.

Applications 716 and 718 shown in connection with interface engine 702are applications developed by third-parties. In some examples, suchapplications include any suitable application that benefits fromaccessing data. The interaction system may include data pertaining tohundreds of thousands of entities. Having data pertaining to so manyentities presents security concerns. For example, much of the data maybe identifying data. Accordingly, data that may be accessed byapplications 716 and 718 may be limited. In some examples, an entity ofthe interaction system may use one of applications 716, 718 to accesshis or her own data. In this example, the identity of the entity may beverified in accordance with techniques described herein.

User devices 706-714 are any suitable user devices capable of runningapplications 716-724. User devices 706-714 are examples of the userdevice 228. In some examples, the user devices include: mobile phones,tablet computers, laptop computers, wearable mobile devices, desktopcomputers, set-top boxes, pagers, and other similar user devices. Insome examples, at least some of user devices 706-714 are the samedevices as at least some of the one or more components 410-418. In someexamples, user devices 706-714 may include complementary layers toapplication/device layer 320 and/or receiving layer 302. For example,user devices 706-714 may include a transmission layer, a generationlayer, and/or a receiving layer to communicate data atapplication/device layer 320 and at receiving layer 302.

Turning now to FIG. 8, an interaction system 800 is shown according toat least one example. Interaction system 800 includes an internalorganization 822 including a transformative processing engine 802. Thetransformative processing engine 802 is an example of transformativeprocessing engine 202 previously discussed. Interaction system 800 isillustrated as an example configuration for implementing the techniquesdescribed herein. In particular, a configuration of elements asillustrated in FIG. 8, at least in some examples, communicates accordingto the layers of architecture stack 300. For example, internalorganization 822 includes generation components 804(1), 804(2), and804(N) which provide data to aggregation servers 806(1)-806(N).

Generation components 804(1), 804(2), and 804(N) operate in accordancewith receiving layer 302. In some examples, generation component 804(1)is a piece of equipment, generation component 804(2) is computer with adata collection device, a type of lab system, and generation component804(N) is a terminal. Aggregation servers 806(1)-806(N) operate inaccordance with aggregation layer 304. Aggregation servers 806(1)-806(N)share data with data storage servers 808(1)-808(N) via one or moreinternal network(s) 810. In some examples, internal network 810 is anysuitable network capable of handling transmission of data. For example,internal network 810 may be any suitable combination of wired orwireless networks. In some examples, internal network 810 may includeone or more secure networks. Data storage servers 808(1)-808(N) areconfigured to store data in accordance with active unified data layer308. Data storage servers 808(1)-808(N) include database servers, filestorage servers, and other similar data storage servers.

Access management servers 812(1)-812(N) manage access to the dataretained in the data storage servers 808(1)-808(N). Access managementservers 812(1)-812(N) communicate with the other elements of interactionsystem 800 via internal network 810 and in accordance with accessmanagement layer 310.

Interface servers 814(1)-814(N) provide one or more interfacesapplications to interact with the other elements of interaction system800. Interface servers 814(1)-814(N) provide the one or more interfacesand communicate with the other elements of interaction system 800 viainternal network 810 and in accordance with interface layer 316. Theinterfaces generated by the interface servers 814(1)-814(N) can be usedby internal user devices 816(1)-816(N) and external user devices 818(1),818(2), and 818(N) to interact with elements of interaction system 800.

Internal user devices 816(1)-816(N) are examples of user devices706-714. In some examples, internal user devices 816(1)-816(N) runapplications via the interfaces generated by interface servers814(1)-814(N). As an additional example, external user devices 818(1),818(2), and 818(N) can run applications developed by third parties thataccess the other elements of interaction system 800 via the interfacesgenerated by interface servers 814(1)-814(N).

External user devices 818(1), 818(2), and 818(N) access the interfacesvia external network 820. In some examples, external network 820 is anunsecured network such as the Internet. External user devices 818(1),818(2), and 818(N) are examples of user devices 706-714. External userdevice 818(1) is a mobile device. In some examples, the mobile devicemay be configured to run an application to access interaction system800. Similarly, the other external user devices 818(2)-818(N) runapplications that enable them to access interaction system 800. Whileinteraction system 800 is shown as implemented using discrete servers,it is understood that it may be implemented using virtual computingresources and/or in a web-based environment.

The systems, environments, devices, components, models, and the like ofFIGS. 1-8 may be used to implement a particular system as describedherein with reference to later figures. In one example, a computer-basedmethod predicts a state of a dependent user based on unique combinationsof first parameter data and second parameter data. The first parameterdata and the second parameter data are received from a data warehouseperiodically and processed. For example, 12 first parameters and 6second parameters may be considered, totaling 278,460 potential uniquecombinations. Each rule set may include a defined high value and/or adefined low value for a plurality of first parameters, a defined highvalue and/or a defined low value for a plurality of second parameters,and an indication of a dependent user type. To trigger a flag based on arule set, both a measured first parameter and a measured secondparameter must be within their respective defined ranges (e.g., high orlow) within a temporal window such as 36 hours. For example, thetemporal window may be measured retrospectively from the current time.The ranges indicate values that are considered to put the dependent userat risk for deterioration. As more flags are triggered, it becomes morelikely that the state of the dependent user is deteriorating. Anotification about the triggered flags may be output. A dependent usermay begin triggering flags on average 24 hours before an event occursthat causes rapid deterioration, such that the flags serve as an earlywarning sign and provide an opportunity for early intervention.

The rule set may be selected as a function of the type of dependentuser. Multiple rule sets may be tuned for multiple types of dependentusers. Multiple rule sets may also be tuned for multiple sub-types ofdependent users. Alternatively or in addition, multiple rule sets mayalso be tuned for different geographic regions and/or dependent userpopulations. Alternatively or in addition, multiple rule sets may alsobe tuned for a specific location. In some examples, the rule sets may betuned by analyzing retrospective data and adjusting the defined highvalues and/or the defined low values of the plurality of firstparameters and/or the plurality of second parameters to maximize thesensitivity.

In another example, a computer-based method uses the flags representingunique combinations of first parameter data and second parameter data todetermine a relevance. For example, the method may know that if flags 1and 10 are triggered, the dependent user is deteriorating and thedeterioration likely has a first cause. However, if flags 1 and 12 aretriggered, the deterioration likely has a second cause that is differentfrom the first cause. Once a state of the dependent user has beendetermined, a set of suggestions can be presented to address the stateof the dependent user. The methods discussed herein may be implementedusing virtual computing resources and/or in a web-based environment.

Referring now to FIG. 9, a block diagram of an example of a network 900is shown. The network 900 includes a transformative integration engine910. The transformative integration engine 910 is an example of thetransformative integration engine 108 discussed with reference toFIG. 1. The transformative integration engine 910 includes anaggregation engine 911, a rule analysis engine 917, and a general datastore 918. The aggregation engine 911 is an example of the aggregationengine 218 discussed with reference to FIG. 2.

Generally the aggregation engine 911 is configured to collect data fromvarious sources and to perform one or more operations on the collecteddata. For example, the aggregation engine 911 may collect first datafrom a first data store 905 and second data from a second data store906. The first data store 905 and the second data store 906 may behoused and/or accessed via a virtual application. The first data store905 may include a Cloudera data stack having raw HL7 data feeds fromelectronic records. The aggregation engine 911 may be associated with aparticular facility within the network 900, with a plurality offacilities within the network 900, or with the entire network 900.Although only a single aggregation engine 911 is shown, a plurality ofaggregation engines may be provided, and each aggregation engine may beassociated with a different facility within the provider network 900.

The first data may include measured first parameters and measured secondparameters for a plurality of dependent users at a facility within thenetwork 900. The first data may also include demographic informationabout each dependent user. In addition, the first data may include aclassification of each dependent user. The first data may also includethe date and time that each measured first parameter and each measuredsecond parameter was measured. The first data may be filtered to removeany invalid and/or impossible results.

The second data may include a plurality of rule sets. Each rule set mayinclude ranges of values for defined first parameters and defined secondparameters. Each of the ranges may be selected such that a measuredvalue within the range may indicate that a state of a dependent user isdeteriorating, and/or that the dependent user will likely betransferred.

The first data and the second data may be updated periodically, such asevery minute, every thirty minutes, every hour, every two hours, everyfour hours, every day, every week, or every month. Further, the firstdata and the second data may be sent to the transformative integrationengine 910 periodically, such as every minute, every thirty minutes,every hour, every two hours, every four hours, every day, every week, orevery month. The aggregation engine 911 may aggregate the dataperiodically at any suitable interval.

The rule analysis engine 917 may determine which of the rule setsapplies in a particular situation. For example, the rule analysis engine917 may select a rule set based on the location of the facility and/orthe dependent user. For example, the ranges of the values within therule set may be different based on the altitude of the facility or thedependent user population of the facility. Alternatively or in addition,the rule analysis engine 917 may select a rule set based on theclassification of the dependent user.

The aggregation engine 911 may aggregate some or all of the data bylayering the rule set on the first data. The aggregation engine 911 mayinclude various engines for aggregating the data, such as a flaggeneration engine 912, an evaluation generation engine 913, a protocolgeneration engine 914, a readmission analysis engine 915, and anadmission analysis engine 916. These engines will be described infurther detail below. After aggregating the data, the aggregation engine911 may send the results to a general data store 918, which isconfigured to store the results. Further, the transformative integrationengine 910 may output the results to a display 920, a user device 930,and/or a centralized server 940.

FIGS. 10, 13, 16, 17, and 18 illustrate example flow diagrams showingprocesses 1000, 1300, 1600, 1700, and 1800, according to at least a fewexamples. These processes, and any other processes described herein, areillustrated as logical flow diagrams, each operation of which representsa sequence of operations that can be implemented in hardware, computerinstructions, or a combination thereof. In the context of computerinstructions, the operations may represent computer-executableinstructions stored on one or more non-transitory computer-readablestorage media that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions includeroutines, programs, objects, components, data structures and the likethat perform particular functions or implement particular data types.The order in which the operations are described is not intended to beconstrued as a limitation, and any number of the described operationscan be combined in any order and/or in parallel to implement theprocesses.

Additionally, some, any, or all of the processes described herein may beperformed under the control of one or more computer systems configuredwith specific executable instructions and may be implemented as code(e.g., executable instructions, one or more computer programs, or one ormore applications) executing collectively on one or more processors, byhardware, or combinations thereof. As noted above, the code may bestored on a non-transitory computer-readable storage medium, forexample, in the form of a computer program including a plurality ofinstructions executable by one or more processors.

Referring now to FIG. 10, a flowchart of a method 1000 according to anexample is shown. The method 1000 may generate at least one flag thatindicate the state of the dependent user. The method 1000 may beperformed by the flag generation engine 912 shown in FIG. 9.

The method 1000 begins at block 1005 where first data is received for adependent user. The first data may be received from first data store905. The first data includes measured first parameters and measuredsecond parameters for the dependent user. Further, the first data mayinclude a classification of the dependent user. The first data may alsoinclude the date and time that each measured first parameter and eachmeasured second parameter was measured.

A rule set for the dependent user is accessed at block 1010. The ruleanalysis engine 917 may identify the rule set based on theclassification of the dependent user and/or the geographic locationwhere the facility is located. The rule set may be accessed from ruleset data store 906. The rule set may include ranges of values fordefined first parameters and defined second parameters for the dependentuser.

The method 1000 then extracts a subset of the first data for thedependent user that falls within a temporal window at block 1015. Thesubset of the first data includes measured first parameters and measuredsecond parameters for the dependent user. The temporal window may haveany suitable duration, such as 12 hours, 24 hours, 36 hours, or 48hours. The duration of the temporal window may be determined by theclassification of the dependent user.

A measured first parameter within the subset of the first data isidentified at block 1020. It is determined whether the measured firstparameter is within the range of values for the corresponding definedfirst parameter at block 1025. The range of values may indicate that thedependent user is more likely to deteriorate. For example, a dependentuser having a white blood cell count of 14,000 per microliter would bewithin the range of values for the white blood cell count of less than3,000 per microliter or greater than 12,000 per microliter.

If it is determined that the measured first parameter is not within therange of values for the corresponding defined first parameter at block1025, then method 1000 determines if there are any additional measuredfirst parameters within the subset of the first data at block 1030. Ifthere are no additional measured first parameters, method 1000 ends atblock 1035. In this example, no flags would be generated for thedependent user. On the other hand, if there are additional measuredfirst parameters, method 1000 returns to block 1020 to identify andanalyze another one of the measured first parameters.

If it is determined that the measured first parameter is within therange of values for the corresponding defined first parameter at block1025, then method 1000 identifies a measured second parameter within thesubset of the first data at block 1040. It is determined whether themeasured second parameter is within the range of values for thecorresponding defined second parameter at block 1045. The range ofvalues may indicate that the dependent user is more likely todeteriorate. For example, a dependent user having a systolic bloodpressure of 80 mm Hg would be within the range of values for thesystolic blood pressure of less than 90 mm Hg.

If it is determined that the measured second parameter is not within therange of values for the corresponding defined second parameter at block1045, then method 1000 determines if there are any additional measuredsecond parameters within the subset of the first data at block 1050. Ifthere are no additional measured second parameters, method 1000 returnsto block 1030, where it is determined if there are any additionalmeasured first parameters. On the other hand, if there are additionalmeasured second parameters, method 1000 returns to block 1040 toidentify and analyze another one of the measured second parameters.

If it is determined that the measured second parameter is within therange of values for the corresponding defined second parameter at block1060, then method 1000 generates a flag at block 1060. The flag includesthe measured first parameter that is within the range of values of thedefined first parameters, as well as the measured second parameter thatis within the range of values of the defined second parameters. Eachflag is a unique combination of a measured first parameter and ameasured second parameter. For example, if there are 12 defined firstparameters and 6 defined second parameters, a total of 72 flags may begenerated as unique combinations of a measured first parameter and ameasured second parameter.

It is determined if there are any additional measured second parameterswithin the subset of the first data at block 1065. If there areadditional measured second parameters, method 1000 returns to block 1040to identify and analyze another one of the measured second parameters.On the other hand, if there are no additional measured secondparameters, it is determined if there are any additional measured firstparameters within the subset of the first data at block 1070. If thereare additional measured first parameters, method 1000 returns to block1020 to identify and analyze another one of the measured firstparameters. If there are no additional measured first parameters, method1000 ends at block 1075 by outputting a message. The message may includeall of the flags that were generated, and each flag includes a uniquecombination of a measured first parameter and a measured secondparameter. Each flag may include an identifier, a value, and ameasurement time of the measured first parameter, as well as anidentifier, a value, and a measurement time of the measured secondparameter. The message may also include additional information about thedependent user. The message may be output to display 920, user device930, and/or centralized server 940. Alternatively or in addition, themessage may be stored in general data store 918.

FIG. 11 shows an example of a rule set 1100 according to an embodimentof the invention. Rule set 1100 includes ranges of values 1110 fordefined first parameters 1105 and ranges of values 1120 for definedsecond parameters 1115. Ranges of values 1110 and/or 1120 are merelyexemplary, and may be adjusted as appropriate. Ranges of values 1110and/or 1120 may include ranges that indicate that the state of adependent user is deteriorating. Further, ranges of values 1110 and/or1120 may include cutoffs to ensure that the ranges only include validvalues. This filters out any invalid results. This may prevent measureddata that was input incorrectly from erroneously generating a flag.

As discussed above, the ranges of values 1110 and/or 1120 within ruleset 1100 may be different based on the location of the dependent user.Further, the ranges of values 1110 and/or 1120 within rule set 1100 maybe different based on the classification or a sub-classification of thedependent user.

FIG. 12 shows an example of a dashboard 1200 that may be generatedaccording to an embodiment of the invention. Dashboard 1200 may includea list of all of the dependent users for whom a flag was generated inthe most recent temporal window. Dashboard 1200 may be displayed ondisplay 920 and/or user device 930.

Dashboard 1200 may include a first column 1210 that lists an identifierof each of a plurality of dependent users. Second column 1212, thirdcolumn 1214, and fourth column 1216 may list characteristics of eachdependent user.

Fifth column 1218 may list the number of unique measured firstparameters that have fallen within the range of values of thecorresponding defined first parameter within the current temporalwindow. Once a measured first parameter has been noted once as fallingwithin the range of values, it is not added again to the number in fifthcolumn 1218 if another measurement of the same measured first parameterfalls within the range of values. For example, if one of a dependentuser's first parameters falls within the range of values for thecorresponding defined first parameter five times within the currenttemporal window, fifth column 1218 only reflects the most recent resultfor the first parameter. Similarly, sixth column 1220 may list thenumber of measured second parameters that have fallen within the rangeof values of the corresponding defined second parameter within thecurrent temporal window. Once a measured second parameter has been notedonce as falling within the range of values, it is not added again to thenumber in sixth column 1220 if another measurement of the same measuredsecond parameter falls within the range of values. For example, if oneof a dependent user's second parameters falls within the range of valuesfor the corresponding defined second parameter three times within thecurrent temporal window, sixth column 1220 only reflects the most recentresult for the second parameter.

Each of seventh column 1222, eighth column 1224, ninth column 1226,tenth column 1228, eleventh column 1230, twelfth column 1232, thirteenthcolumn 1234, fourteenth column 1236, fifteenth column 1238, sixteenthcolumn 1240, and seventeenth column 1242 lists the most recentmeasurement of a respective measured first parameter that has fallenwithin the range of values of the corresponding defined first parameterswithin the current temporal window. Each of seventh column 1222, eighthcolumn 1224, ninth column 1226, tenth column 1228, eleventh column 1230,twelfth column 1232, thirteenth column 1234, fourteenth column 1236,fifteenth column 1238, sixteenth column 1240, and seventeenth column1242 includes the value of the measured first parameter, the date onwhich the measurement was taken, and the time at which the measurementwas taken. Each of seventh column 1222, eighth column 1224, ninth column1226, tenth column 1228, eleventh column 1230, twelfth column 1232,thirteenth column 1234, fourteenth column 1236, fifteenth column 1238,sixteenth column 1240, and seventeenth column 1242 may also include anytrends in the measured first parameter during the current temporalwindow (not shown). For example, any measurements of the measured firstparameter that have fallen within the range of values of thecorresponding defined measured first parameter within the currenttemporal window may be included.

Each of eighteenth column 1244, nineteenth column 1246, twentieth column1248, twenty-first column 1250, and twenty-second column 1252 lists themost recent measurement of a respective measured second parameter thathas fallen within the range of values of the corresponding definedrespiratory rate within the current temporal window. Each of eighteenthcolumn 1244, nineteenth column 1246, twentieth column 1248, twenty-firstcolumn 1250, and twenty-second column 1252 includes the value of themeasured second parameter, the date on which the measurement was taken,and the time at which the measurement was taken. Each of eighteenthcolumn 1244, nineteenth column 1246, twentieth column 1248, twenty-firstcolumn 1250, and twenty-second column 1252 may also include any trendsin the measured second parameter during the current temporal window (notshown). For example, any measurements of the measured second parameterthat have fallen within the range of values of the corresponding definedmeasured second parameter within the current temporal window may beincluded.

Twenty-third column 1254 is a text entry field. Twenty-fourth column1256 indicates whether an alert generated by the system was correct. Forexample, if at least one flag is generated for a dependent user, a basicalert is generated. A detailed alert is then sent to an authorized user,including the measured first parameters and the measured secondparameters that generated the flags, along with a time frame for aresponse. The authorized user may indicate whether the alert wascorrect. For example, the alert may be incorrect if a subsequentmeasurement of the dependent user's first parameters and/or secondparameters is outside of the range that triggered the flag.

Twenty-fifth column 1258 indicates whether a dependent user for whom adetailed was sent has been evaluated by an authorized user. Twenty-sixthcolumn 1260 indicates whether the authorized user has been notified ofthe alert. Specifically, the twenty-sixth column 1260 indicates whetherthe detailed alert has been sent.

Referring now to FIG. 13, a flowchart of a method 1300 according to anexample is shown. The method 1300 may generate a plurality of alertsrelating to the state of a dependent user. The method 1200 may beperformed by the flag generation engine 912 shown in FIG. 9.

The method 1300 begins at block 1305 where a basic alert is transmittedfor a dependent user. The basic alert may include an identifier and alocation of the dependent user. The basic alert may be sent bycentralized server 940 to a first authorized user via SMS message. Thefirst authorized user may receive the basic alert via user device 930.

A time frame of response for the dependent user is determined at block1310. For example, the authorized user may view the dashboard 1200 shownin FIG. 12, and use the dashboard 1200 to determine the time frame thatis needed. For example, the level of response may be a standard responseto be completed within 90 minutes, a sepsis alert response to becompleted within 30 minutes, or a rapid response to be completed within15 minutes.

A detailed alert for the dependent user is sent at block 1315. Thedetailed alert may include an identifier of the dependent user, alocation of the dependent user, the measured first parameters and themeasured second parameters that generated the flags and the time framefor the response. The detailed alert may be transmitted via SMS messageto a device of a second authorized user. The detailed alert may betransmitted within 17 minutes of the triggering of the flag.

The state of the dependent user is determined at block 1320. Theauthorized user may determine the state of the dependent user within thetime frame listed in the detailed alert. Further, the second authorizeduser may adjust the protocol for the dependent user based on thedetermination.

The protocol for the dependent user may be implemented at block 1325.Any actions taken for the dependent user may be reported to centralizedserver 940 via SMS message at block 1330. For example, the secondauthorized user may use a device to transmit any changes to the protocolfor the dependent user, any additional first parameters or secondparameters that were obtained, and any implemented portions of theprotocol for the dependent user.

The actions taken for the dependent user may be documented at block1335. For example, the dashboard 1200 shown in FIG. 12 may be updated. Alog of the first authorized user may also be updated.

FIG. 14 shows an example of a plurality of alerts 1400 that may begenerated according to an embodiment of the invention. Alerts 1400 areexamples of the detailed alert that may be sent at block 1315 of method1300 shown in FIG. 13.

FIG. 15 shows an example of a plurality of messages 1500 that may begenerated according to an embodiment of the invention. Messages 1500 areexamples of the reports that may be sent at block 1330 of method 1300shown in FIG. 13.

Referring now to FIG. 16, a flowchart of a method 1600 according to anexample is shown. The method 1600 may determine a candidate evaluationand/or a candidate protocol for a dependent user based on the flags thatare generated for the dependent user. The method 1600 may be performedby the evaluation generation engine 913 and/or the protocol generationengine 914 shown in FIG. 9.

The method 1600 begins at block 1605 where at least one flag is accessedfor a dependent user. Each flag may be generated by method 1000 shown inFIG. 10. Each flag may be accessed from general data store 918 shown inFIG. 9. Each flag includes a measured first parameter that is within therange of values for the respective defined first parameter and ameasured second parameter that is within the range of values for therespective defined second parameter. The measured first parameter andthe measured second parameter are measured within the same temporalwindow.

At least one candidate evaluation of a state of the dependent user maybe determined at block 1610. The candidate evaluation may be determinedby inputting the dependent user's flags as parameters into an artificialintelligence engine, which may compare the dependent user's flagsagainst a database of historical dependent user data that includesevaluations made for various combinations of the measured firstparameters and the measured second parameters within the flags. At leastone candidate protocol for the dependent user may be determined at block1615.

Alternatively, it may not be necessary to determine a single evaluationof the dependent user's state. Instead, the dependent user's flags maybe analyzed to generate a set of differential evaluations for thedependent user. For example, three candidate evaluations may begenerated and ranked according to likelihood, based on the components ofthe flags for the dependent user. The candidate protocol may bedetermined based on the differential evaluations.

A message including the candidate evaluation and/or the candidateprotocol for the dependent user may be output at block 1620 to display920, user device 930, and/or centralized server 940. Alternatively or inaddition, the message may be stored in general data store 918.

Referring now to FIG. 17, a flowchart of a method 1700 according to anexample is shown. The method 1700 may determine a likelihood ofreadmission for a dependent user based on the flags that are generatedfor the dependent user. The method 1700 may be performed by thereadmission analysis engine 915 shown in FIG. 9.

The method 1700 begins at block 1705 where at least one flag is accessedfor a dependent user. Each flag may be generated by method 1000 shown inFIG. 10. Each flag may be accessed from general data store 918 shown inFIG. 9. Each flag includes a measured first parameter that is within therange of values for the respective defined first parameter and ameasured second parameter that is within the range of values for therespective defined second parameter. The measured first parameter andthe measured second parameter are measured within the same temporalwindow.

A likelihood of readmission for the dependent user may be determined atblock 1710. The likelihood of readmission may be determined by inputtingthe dependent user's flags as parameters into an artificial intelligenceengine, which may compare the dependent user's flags against a databaseof historical dependent user data that includes readmission rates forvarious combinations of the measured first parameters and the measuredsecond parameters within the flags.

A message including the likelihood of readmission for the dependent usermay be output at block 1715 to display 920, user device 930, and/orcentralized server 940. Alternatively or in addition, the message may bestored in general data store 918.

Referring now to FIG. 18, a flowchart of a method 1800 according to anexample is shown. The method 1800 may determine a level for a dependentuser based on the flags that are generated for the dependent user. Themethod 1800 may be performed by the admission analysis engine 916 shownin FIG. 9.

The method 1800 begins at block 1805 where at least one flag is accessedfor a dependent user. Each flag may be generated by method 1000 shown inFIG. 10. Each flag may be accessed from general data store 918 shown inFIG. 9. Each flag includes a measured first parameter that is within therange of values for the respective defined first parameter and ameasured second parameter that is within the range of values for therespective defined second parameter. The measured first parameter andthe measured second parameter are measured within the same temporalwindow.

A level for the dependent user may be determined at block 1810. Forexample, a suitable number of first parameters and second parameters maybe obtained for the dependent user. The flags generated may be used todetermine the level of the dependent user. The level may be determinedby inputting the dependent user's flags as parameters into an artificialintelligence engine, which may compare the dependent user's flagsagainst a database of historical dependent user data that includes thelevel for various combinations of the measured first parameters and themeasured second parameters within the flags.

A message including the level for the dependent user may be output atblock 1815 to display 920, user device 930, and/or centralized server940. Alternatively or in addition, the message may be stored in generaldata store 918.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments may be practiced without these specific details.For example, circuits may be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquesmay be shown without unnecessary detail in order to avoid obscuring theembodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A computer-implemented method of determining astate of a dependent user, the computer-implemented method comprising:storing rules in a non-transitory, machine-readable storage medium,where the rules comprise machine-learned rules; using themachine-learned rules to execute one or more data query scripts to queryone or more data stores, and, consequent to execution of the one or moredata query scripts in accordance with the machine-learned rules,receiving data for the dependent user from a data warehouse, wherein:the data comprises a plurality of measured laboratory results and aplurality of measured vital signs, the data is provided to the datawarehouse from a plurality of data sources, and at least some of thedata is converted according to conversion rules of the rules to a secondformat from a first format in which the at least some of the data isprovided by at least one data source of the plurality of data sources;accessing a rule set of the rules for the dependent user, wherein therule set comprises a range of values for each of a plurality of definedlaboratory results and a range of values for each of a plurality ofdefined vital signs, and the rule set is a function of a location of thedependent user; for a subset of the data that is collected within atemporal window: for at least one measured laboratory result of theplurality of measured laboratory results, comparing the measuredlaboratory result with a respective one of the plurality of the definedlaboratory results to determine whether the measured laboratory resultis within the range of values for the respective one of the plurality ofdefined laboratory results; for at least one measured vital sign of theplurality of measured vital signs, comparing the measured vital signwith a respective one of the plurality of the defined vital signs todetermine whether the measured vital sign is within the range of valuesfor the respective one of the plurality of defined vital signs; and ifit is determined that at least one of the measured laboratory results iswithin the range of values for the respective one of the plurality ofthe defined laboratory results and it is determined that at least one ofthe measured vital signs is within the range of values for therespective one of the plurality of the defined vital signs: generating aflag indicating that the state of the dependent user is deteriorating;outputting a message comprising an identifier of the dependent user, theflag, the at least one of the measured laboratory results that is withinthe range of values for the respective one of the plurality of thedefined laboratory results, and the at least one of the measured vitalsigns is within the range of values for the respective one of theplurality of the defined vital signs; determining at least one candidatediagnosis of a condition of the dependent user as a function of theflag; determining at least one candidate treatment of the dependent useras a function of the at least one candidate diagnosis; and treating thedependent user according to the at least one candidate treatment.
 2. Thecomputer-implemented method of claim 1, further comprising processingthe data to filter out invalid entries.
 3. The computer-implementedmethod of claim 1, wherein the rule set is a function of aclassification of the dependent user.
 4. The computer-implemented methodof claim 1, wherein the message further comprises a level of responsefor the state of the dependent user.
 5. The computer-implemented methodof claim 4, wherein the message further comprises a time frame for theresponse.
 6. A system of determining a state of a dependent user, thesystem comprising: one or more data processors; and a non-transitory,computer-readable storage medium containing instructions that, whenexecuted on the one or more data processors, cause the one or more dataprocessors to perform actions including: storing rules comprisingmachine-learned rules; using the machine-learned rules to execute one ormore data query scripts to query one or more data stores, and,consequent to execution of the one or more data query scripts inaccordance with the machine-learned rules, receiving data for thedependent user from a data warehouse, wherein: the data comprises aplurality of measured laboratory results and a plurality of measuredvital signs, the data is provided to the data warehouse from a pluralityof data sources, and at least some of the data is converted according toconversion rules of the rules to a second format from a first format inwhich the at least some of the data is provided by at least one datasource of the plurality of data sources; accessing a rule set of therules for the dependent user, wherein the rule set comprises a range ofvalues for each of a plurality of defined laboratory results and a rangeof values for each of a plurality of defined vital signs, and the ruleset is a function of a location of the dependent user; for a subset ofthe data that is collected within a temporal window: for at least onemeasured laboratory result of the plurality of measured laboratoryresults, comparing the measured laboratory result with a respective oneof the plurality of the defined laboratory results to determine whetherthe measured laboratory result is within the range of values for therespective one of the plurality of defined laboratory results; for atleast one measured vital sign of the plurality of measured vital signs,comparing the measured vital sign with a respective one of the pluralityof the defined vital signs to determine whether the measured vital signis within the range of values for the respective one of the plurality ofdefined vital signs; and if it is determined that at least one of themeasured laboratory results is within the range of values for therespective one of the plurality of the defined laboratory results and itis determined that at least one of the measured vital signs is withinthe range of values for the respective one of the plurality of thedefined vital signs: generating a flag indicating that the state of thedependent user is deteriorating; outputting a message comprising anidentifier of the dependent user, the flag, the at least one of themeasured laboratory results that is within the range of values for therespective one of the plurality of the defined laboratory results, andthe at least one of the measured vital signs is within the range ofvalues for the respective one of the plurality of the defined vitalsigns; determining at least one candidate diagnosis of a condition ofthe dependent user as a function of the flag; determining at least onecandidate treatment of the dependent user as a function of the at leastone candidate diagnosis; and treating the dependent user according tothe at least one candidate treatment.
 7. The system of claim 6, whereinthe actions further include processing the data to filter out invalidentries.
 8. The system of claim 6, wherein the rule set is a function ofa classification of the dependent user.
 9. The system of claim 6,wherein the message further comprises a level of response for the stateof the dependent user.
 10. The system of claim 9, wherein the messagefurther comprises a time frame for the response.
 11. A computer-programproduct tangibly embodied in a non-transitory, machine-readable storagemedium, including instructions configured to cause one or more dataprocessors to perform actions for determining a state of a dependentuser, the actions including: storing rules comprising machine-learnedrules; using the machine-learned rules to execute one or more data queryscripts to query one or more data stores, and, consequent to executionof the one or more data query scripts in accordance with themachine-learned rules, receiving data for the dependent user from a datawarehouse, wherein: the data comprises a plurality of measuredlaboratory results and a plurality of measured vital signs, the data isprovided to the data warehouse from a plurality of data sources, and atleast some of the data is converted according to conversion rules of therules to a second format from a first format in which the at least someof the data is provided by at least one data source of the plurality ofdata sources; accessing a rule set of the rules for the dependent user,wherein the rule set comprises a range of values for each of a pluralityof defined laboratory results and a range of values for each of aplurality of defined vital signs, and the rule set is a function of alocation of the dependent user; for a subset of the data that iscollected within a temporal window: for at least one measured laboratoryresult of the plurality of measured laboratory results, comparing themeasured laboratory result with a respective one of the plurality of thedefined laboratory results to determine whether the measured laboratoryresult is within the range of values for the respective one of theplurality of defined laboratory results; for at least one measured vitalsign of the plurality of measured vital signs, comparing the measuredvital sign with a respective one of the plurality of the defined vitalsigns to determine whether the measured vital sign is within the rangeof values for the respective one of the plurality of defined vitalsigns; and if it is determined that at least one of the measuredlaboratory results is within the range of values for the respective oneof the plurality of the defined laboratory results and it is determinedthat at least one of the measured vital signs is within the range ofvalues for the respective one of the plurality of the defined vitalsigns: generating a flag indicating that the state of the dependent useris deteriorating; outputting a message comprising an identifier of thedependent user, the flag, the at least one of the measured laboratoryresults that is within the range of values for the respective one of theplurality of the defined laboratory results, and the at least one of themeasured vital signs is within the range of values for the respectiveone of the plurality of the defined vital signs; determining at leastone candidate diagnosis of a condition of the dependent user as afunction of the flag; determining at least one candidate treatment ofthe dependent user as a function of the at least one candidatediagnosis; and treating the dependent user according to the at least onecandidate treatment.
 12. The computer-program product of claim 11,wherein the actions further include processing the data to filter outinvalid entries.
 13. The computer-program product of claim 11, whereinthe rule set is a function of a classification of the dependent user.14. The computer-program product of claim 11, wherein the messagefurther comprises a level of response for the state of the dependentuser.
 15. The computer-program product of claim 14, wherein the messagefurther comprises a time frame for the response.